






"""simple docstring"""







import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )







class    lowerCamelCase__   (			unittest.TestCase					):






    def __init__(					self      ,A      ,A=13      ,A=7      ,A=True      ,A=True      ,A=True      ,A=True      ,A=99      ,A=32      ,A=5      ,A=4      ,A=37      ,A="gelu"      ,A=0.1      ,A=0.1      ,A=512      ,A=16      ,A=2      ,A=0.02      ,A=4      ,):
          UpperCAmelCase							     =			parent
          UpperCAmelCase							     =			batch_size
          UpperCAmelCase							     =			seq_length
          UpperCAmelCase							     =			is_training
          UpperCAmelCase							     =			use_attention_mask
          UpperCAmelCase							     =			use_token_type_ids
          UpperCAmelCase							     =			use_labels
          UpperCAmelCase							     =			vocab_size
          UpperCAmelCase							     =			hidden_size
          UpperCAmelCase							     =			num_hidden_layers
          UpperCAmelCase							     =			num_attention_heads
          UpperCAmelCase							     =			intermediate_size
          UpperCAmelCase							     =			hidden_act
          UpperCAmelCase							     =			hidden_dropout_prob
          UpperCAmelCase							     =			attention_probs_dropout_prob
          UpperCAmelCase							     =			max_position_embeddings
          UpperCAmelCase							     =			type_vocab_size
          UpperCAmelCase							     =			type_sequence_label_size
          UpperCAmelCase							     =			initializer_range
          UpperCAmelCase							     =			num_choices






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size   )

          UpperCAmelCase							     =			None
          if self.use_attention_mask:
                UpperCAmelCase							     =			random_attention_mask([self.batch_size, self.seq_length]   )

          UpperCAmelCase							     =			None
          if self.use_token_type_ids:
                UpperCAmelCase							     =			ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size   )

          UpperCAmelCase							     =			RoFormerConfig(
              vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,is_decoder=snake_case_      ,initializer_range=self.initializer_range      ,)

          return config, input_ids, token_type_ids, attention_mask






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.prepare_config_and_inputs()
          UpperCAmelCase							     =			config_and_inputs
          UpperCAmelCase							     =			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
          return config, inputs_dict







@require_flax
class    lowerCamelCase__   (			_A       ,	unittest.TestCase					):
    SCREAMING_SNAKE_CASE						=     True

    SCREAMING_SNAKE_CASE						=     (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxRoFormerModelTester(self   )






    @slow
    def 							_UpperCamelCase				(					self   ):
          for model_class_name in self.all_model_classes:
                UpperCAmelCase							     =			model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""      ,from_pt=snake_case_   )
                UpperCAmelCase							     =			model(np.ones((1, 1)   )   )
                self.assertIsNotNone(snake_case_   )







@require_flax
class    lowerCamelCase__   (			unittest.TestCase					):






    @slow
    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
          UpperCAmelCase							     =			jnp.array([[0, 1, 2, 3, 4, 5]]   )
          UpperCAmelCase							     =			model(snake_case_   )[0]

          UpperCAmelCase							     =			50_000

          UpperCAmelCase							     =			(1, 6, vocab_size)
          self.assertEqual(output.shape      ,snake_case_   )

          UpperCAmelCase							     =			jnp.array(
              [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

          self.assertTrue(jnp.allclose(output[:, :3, :3]      ,snake_case_      ,atol=1e-4   )   )





import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			@require_torch
			def    lowerCAmelCase__						(							self			:							List[str]   ):
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCamelCase_:				Optional[Any]						=    """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

							UpperCamelCase_:				List[Any]						=    """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

							UpperCamelCase_:				Dict						=    """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")
socket.socket = offline_socket
        """

							# Force fetching the files so that we can use the cache
							UpperCamelCase_:				int						=    """hf-internal-testing/tiny-random-bert"""
							BertConfig.from_pretrained(snake_case_   )
							BertModel.from_pretrained(snake_case_   )
							BertTokenizer.from_pretrained(snake_case_   )
							pipeline(task="""fill-mask"""    ,						model=snake_case_   )

							# baseline - just load from_pretrained with normal network
							UpperCamelCase_:				Tuple						=    [sys.executable, """-c""", """\n""".join([load, run, mock]   )]

							# should succeed
							UpperCamelCase_:				Optional[int]						=    self.get_env()
							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCamelCase_:				int						=    """1"""
							UpperCamelCase_:				Optional[Any]						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						0    ,						result.stderr   )
							self.assertIn("""success"""    ,						result.stdout.decode()   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							Any   ):
							# python one-liner segments
							# this must be loaded before socket.socket is monkey-patched
							UpperCamelCase_:				str						=    """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
        """

							UpperCamelCase_:				Any						=    """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
        """

							UpperCamelCase_:				Optional[int]						=    """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")
socket.socket = offline_socket
        """

							# Force fetching the files so that we can use the cache
							UpperCamelCase_:				Optional[int]						=    """hf-internal-testing/tiny-random-bert"""
							BertConfig.from_pretrained(snake_case_   )
							BertModel.from_pretrained(snake_case_   )
							BertTokenizer.from_pretrained(snake_case_   )
							pipeline(task="""fill-mask"""    ,						model=snake_case_   )

							# baseline - just load from_pretrained with normal network
							UpperCamelCase_:				Optional[int]						=    [sys.executable, """-c""", """\n""".join([load, run, mock]   )]

							# should succeed
							UpperCamelCase_:				List[str]						=    self.get_env()
							UpperCamelCase_:				List[Any]						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						0    ,						result.stderr   )
							self.assertIn("""success"""    ,						result.stdout.decode()   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							int   ):
							# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
							# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
							# while running an external program

							# python one-liner segments

							# this must be loaded before socket.socket is monkey-patched
							UpperCamelCase_:				Dict						=    """
from transformers import BertConfig, BertModel, BertTokenizer
        """

							UpperCamelCase_:				Optional[Any]						=    """
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
        """

							UpperCamelCase_:				Dict						=    """
import socket
def offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")
socket.socket = offline_socket
        """

							# baseline - just load from_pretrained with normal network
							UpperCamelCase_:				Union[str, Any]						=    [sys.executable, """-c""", """\n""".join([load, run]   )]

							# should succeed
							UpperCamelCase_:				Union[str, Any]						=    self.get_env()
							UpperCamelCase_:				List[Any]						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						0    ,						result.stderr   )
							self.assertIn("""success"""    ,						result.stdout.decode()   )

							# next emulate no network
							UpperCamelCase_:				Optional[int]						=    [sys.executable, """-c""", """\n""".join([load, mock, run]   )]

							# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
							# env["TRANSFORMERS_OFFLINE"] = "0"
							# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
							# self.assertEqual(result.returncode, 1, result.stderr)

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCamelCase_:				Union[str, Any]						=    """1"""
							UpperCamelCase_:				int						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						0    ,						result.stderr   )
							self.assertIn("""success"""    ,						result.stdout.decode()   )

			@require_torch
			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Dict						=    """
from transformers import pipeline
        """
							UpperCamelCase_:				Union[str, Any]						=    """
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
        """

							UpperCamelCase_:				Dict						=    """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
        """
							UpperCamelCase_:				Optional[Any]						=    self.get_env()
							UpperCamelCase_:				int						=    """1"""
							UpperCamelCase_:				int						=    [sys.executable, """-c""", """\n""".join([load, mock, run]   )]
							UpperCamelCase_:				Any						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						1    ,						result.stderr   )
							self.assertIn(
							    """You cannot infer task automatically within `pipeline` when using offline mode"""    ,						result.stderr.decode().replace("""\n"""    ,						""""""   )    ,						)




			@require_torch
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				str						=    """
from transformers import AutoModel
        """
							UpperCamelCase_:				Dict						=    """
mname = \"hf-internal-testing/test_dynamic_model\"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print(\"success\")
        """

							# baseline - just load from_pretrained with normal network
							UpperCamelCase_:				Optional[int]						=    [sys.executable, """-c""", """\n""".join([load, run]   )]

							# should succeed
							UpperCamelCase_:				Optional[Any]						=    self.get_env()
							UpperCamelCase_:				int						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						0    ,						result.stderr   )
							self.assertIn("""success"""    ,						result.stdout.decode()   )

							# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
							UpperCamelCase_:				Union[str, Any]						=    """1"""
							UpperCamelCase_:				List[str]						=    subprocess.run(snake_case_    ,						env=snake_case_    ,						check=snake_case_    ,						capture_output=snake_case_   )
							self.assertEqual(result.returncode    ,						0    ,						result.stderr   )
							self.assertIn("""success"""    ,						result.stdout.decode()   )






